Wingo writes about day two of the International Lisp Conference, covering talks on David Moon's PLOT language with its hygienic macro system, Joe Marshall's approach to implementing continuations on the JVM or CLR, and a panel debate on the future of Lisp. The event featured a staged debate on whether macros are a menace, during which Richard Gabriel jokingly drew a frowny face on the chalkboard using the "..." in a macro definition. Sussman also explained MIT's decision to switch from Scheme to Python for their introductory course, citing the need for a more robust, robotics-based curriculum to match modern software practices.
- Comments revealed that David Moon, the author of PLOT, had significant historical contributions to Lisp, including inventing ephemeral garbage collection and naming Common Lisp.
- Scott McKay, a panelist, helped create Spice Lisp and designed CLIM, a standard GUI toolkit for Common Lisp.
- The debate on macros included a humorous exchange where Jeremy H. Brown compared macros to a laboratory mechanism for experimentation and taste.
- Comments revealed that David Moon, the author of PLOT, had significant historical contributions to Lisp, including inventing ephemeral garbage collection and naming Common Lisp.
- Scott McKay, a panelist, helped create Spice Lisp and designed CLIM, a standard GUI toolkit for Common Lisp.
- The debate on macros included a humorous exchange where Jeremy H. Brown compared macros to a laboratory mechanism for experimentation and taste.
An interview with Michael Tomczyk, former assistant to Commodore's Jack Tramiel, who describes how the company deliberately developed the VIC-20 and Commodore 64 to keep Japanese computer makers from entering the Western market. He recounts his "bear in the woods" strategy of releasing a low-cost computer to slow rivals down while a better one was built, and how Japanese teams contributed significantly to VIC design, software, and early game cartridges through a young company called HAL Laboratory. Tomczyk also reveals the existence of a more advanced prototype called the MAX—a next-generation machine with built-in wordprocessing, spreadsheet, database, and speech capabilities—that was killed when new management shifted strategy toward business computers after Tramiel's departure.
- Tomczyk negotiated a Nintendo game porting deal that Jack Tramiel cancelled at the last minute, possibly because of a rival arrangement with Bally-Midway; Tomczyk believes his pitch helped push Nintendo into game consoles.
- His original name for the VIC-20 was "Commodore Spirit," abandoned after Japanese colleagues explained the word carried connotations of ghoulish things in their culture.
- The first $100 direct-connect modem on a cartridge was Tomczyk's idea, outsourced to a small engineering firm because Commodore's own team was overloaded.
- In May 1984, roughly 35 top Commodore employees—including Tomczyk, the U.S. president, and key engineers—left in a single week; Japanese vice president Taro Tokai joined Atari, which Tramiel then bought.
- Tomczyk mocked MSX as "MS-DEAD," believing the 12-company Asian consortium had been tricked into adopting two-year-old, blocky technology that arrived in the U.S. stillborn.
- Tomczyk negotiated a Nintendo game porting deal that Jack Tramiel cancelled at the last minute, possibly because of a rival arrangement with Bally-Midway; Tomczyk believes his pitch helped push Nintendo into game consoles.
- His original name for the VIC-20 was "Commodore Spirit," abandoned after Japanese colleagues explained the word carried connotations of ghoulish things in their culture.
- The first $100 direct-connect modem on a cartridge was Tomczyk's idea, outsourced to a small engineering firm because Commodore's own team was overloaded.
- In May 1984, roughly 35 top Commodore employees—including Tomczyk, the U.S. president, and key engineers—left in a single week; Japanese vice president Taro Tokai joined Atari, which Tramiel then bought.
- Tomczyk mocked MSX as "MS-DEAD," believing the 12-company Asian consortium had been tricked into adopting two-year-old, blocky technology that arrived in the U.S. stillborn.
OpenAI writes that their latest models show major gains in coding with low-resource languages like COBOL, Fortran, and Visual Basic, which support critical infrastructure but lack training data compared to Python and JavaScript. To measure progress, they developed two evaluations: MultiLangBench, which asks a model to build a SQLite-compatible database engine from scratch in a given language, and a COBOL migration benchmark that tests behavior-preserving rewrites to other languages. GPT‑6 Astra reached an 87.4% average pass rate on MultiLangBench across 20 languages, up from 26.8% for the prior GPT‑5.6 Sol, while GPT‑6.1 Sol achieved 86.6% on the COBOL migration test at low reasoning effort and 97.9% at high effort.
- MultiLangBench uses SQLite database files as a shared interface so the same tests can be applied across all 20 languages.
- COBOL migration requires byte-for-byte parity on observable outputs, catching subtle issues like COBOL's fixed-point arithmetic discarding decimal digits unless rounding is explicitly requested.
- GPT‑6 Luna offers a lower-cost alternative, reaching 40.3% on MultiLangBench at roughly 78% lower API cost than GPT‑5.6 Sol.
- The evaluation setup and test suite are released on GitHub.
- MultiLangBench uses SQLite database files as a shared interface so the same tests can be applied across all 20 languages.
- COBOL migration requires byte-for-byte parity on observable outputs, catching subtle issues like COBOL's fixed-point arithmetic discarding decimal digits unless rounding is explicitly requested.
- GPT‑6 Luna offers a lower-cost alternative, reaching 40.3% on MultiLangBench at roughly 78% lower API cost than GPT‑5.6 Sol.
- The evaluation setup and test suite are released on GitHub.
Thomas Reid writes about OpenAI's new Decisions API, a dedicated endpoint that makes fast, typed decisions such as classifying support requests, detecting missing information in documents, and inspecting product images. The API supports predicate, choice, and score question types and runs on the gpt-6-luna LLM, claiming up to 10x speed over standard LLM calls. Through three Python examples, the article compares the Decisions API to TypesafeAI's JEV model, concluding that Decisions has the edge on image handling while JEV leads on speed and input cost.
- The API costs $0.10 per million input tokens with no charges for output, cache reads, or cache writes.
- JEV costs $0.042 per million input tokens, making it 58% cheaper on input.
- Images must be sent as inline data URLs; standard web URLs and file IDs are not accepted.
- The endpoint supports up to 128 images per request.
- The API costs $0.10 per million input tokens with no charges for output, cache reads, or cache writes.
- JEV costs $0.042 per million input tokens, making it 58% cheaper on input.
- Images must be sent as inline data URLs; standard web URLs and file IDs are not accepted.
- The endpoint supports up to 128 images per request.
Fallahpour and colleagues write about Memorilla, a memory system that compresses an entire document collection into a fixed set of sixteen query-dependent memory tokens injected into a frozen LLM's input embedding space, outperforming baselines like Mem0 and MemoRAG on five of eight benchmarks spanning personalization, long-document, and knowledge-intensive question answering.
- Memorilla encodes documents once into reusable embeddings, then learns to retrieve and place query-specific information directly in the LLM's input embedding space.
- On FactKG it reaches 84.33% accuracy versus 80.43% with full-context inference, while using 108x fewer tokens to represent the source documents.
- In a TextWorld agentic environment, Memorilla raises average success rate from 18.6% (full-history in-context learning) to 24.2% and reduces the agent's tendency to re-explore rooms it has already visited.
- The work is positioned as a step toward continually learning systems, where the document count grows as an agent accumulates past turns.
- Memorilla encodes documents once into reusable embeddings, then learns to retrieve and place query-specific information directly in the LLM's input embedding space.
- On FactKG it reaches 84.33% accuracy versus 80.43% with full-context inference, while using 108x fewer tokens to represent the source documents.
- In a TextWorld agentic environment, Memorilla raises average success rate from 18.6% (full-history in-context learning) to 24.2% and reduces the agent's tendency to re-explore rooms it has already visited.
- The work is positioned as a step toward continually learning systems, where the document count grows as an agent accumulates past turns.
Natalie Wolchover writes that the 2014 invention of the attention mechanism by Dzmitry Bahdanau, Yoshua Bengio, and Kyunghyun Cho unlocked scalable machine translation, which became the foundation for transformer architectures and the broader large language model revolution. Wolchover examines how attention functions as a mathematical operation allowing neural networks to link words and build context, and whether this mechanism represents the core of intelligence. She concludes that while language modeling is so demanding it may be "AI-complete," human thought likely relies on non-linguistic processes as well, and that current transformer architectures remain far less efficient than the human brain at accomplishing similar tasks.
- Bahdanau was a 23-year-old intern at the University of Montreal in 2014 who described attention as "just obviously the right thing to do."
- The Google paper "Attention Is All You Need" (2017) has been cited more than 250,000 times.
- Llion Jones, co-author of "Attention Is All You Need" and CTO of Sakana AI, now believes attention is not sufficient for truly masterful language modeling and that a fundamentally different paradigm is needed.
- A 2024 Nature study suggests human thought activates more brain regions than just language networks, indicating intelligence is not purely linguistic.
- Bahdanau was a 23-year-old intern at the University of Montreal in 2014 who described attention as "just obviously the right thing to do."
- The Google paper "Attention Is All You Need" (2017) has been cited more than 250,000 times.
- Llion Jones, co-author of "Attention Is All You Need" and CTO of Sakana AI, now believes attention is not sufficient for truly masterful language modeling and that a fundamentally different paradigm is needed.
- A 2024 Nature study suggests human thought activates more brain regions than just language networks, indicating intelligence is not purely linguistic.
Debashis Das writes that M5Stack has released the PaperDIY series, an updated version of its 2020 M5Paper kit that pairs an ESP32-S3 MCU with a 4.7-inch e-paper display supporting 16 levels of gray. The product is available in two versions: a Complete Kit that ships factory-assembled in a 3D-printed enclosure, and an Assembly Kit designed for custom projects. Both boards include a microSD card slot, a built-in 140mAh battery, and a custom M5PM1 low-power management chip.
- The display uses an 8-bit parallel interface, enabling direct control without a secondary display controller
- M5Stack provides factory firmware source code based on ESP-IDF, including examples for battery monitoring and Wi-Fi scanning
- A critical warning notes that mounting screws must have a thread length under 3mm to avoid permanently damaging the e-paper display
- The Assembly Kit retails for $25, while the Complete Kit costs $29.90
- The display uses an 8-bit parallel interface, enabling direct control without a secondary display controller
- M5Stack provides factory firmware source code based on ESP-IDF, including examples for battery monitoring and Wi-Fi scanning
- A critical warning notes that mounting screws must have a thread length under 3mm to avoid permanently damaging the e-paper display
- The Assembly Kit retails for $25, while the Complete Kit costs $29.90
Choi In-jun writes about how AI tools are enabling rapid reverse engineering of commercial software, threatening established players like Adobe and Microsoft. Developer Brandon Thomas used AI to replicate features of Adobe's creative suite and Microsoft Office programs, releasing them for free and prompting HSBC to downgrade Adobe's stock rating. A new open-source tool called REA (Reverse Engineer Anything) connects AI coding assistants with specialized reverse engineering tools, dramatically reducing the time and cost needed to analyze and replicate software functionality.
- A Chosun reporter demonstrated that REA could analyze an open-source document editor in ~7 seconds, with AI coding tools then recreating core editing features and a working interface in under 7 minutes total.
- The NSA's Ghidra, a reverse engineering tool hidden for nearly 20 years and released publicly in 2019, previously made analysis impractical due to missing comments and massive code size; AI has largely overcome this barrier.
- A South Korean developer released "Tangle 2026," which can read and edit HWP/HWPX files used by Hancom's Hangul, representing a domestic attempt at AI-driven software replication.
- Opinions in the industry are split: Dutch developer Pieter Levels argues users will build only the features they need rather than pay subscriptions, while Brazilian developer Fábio Akita counters that AI-generated alternatives contain errors and cannot fully replicate complex commercial products.
- South Korea's Copyright Act permits code analysis for compatibility or operational understanding but prohibits using obtained information beyond those purposes, setting the stage for legal disputes as replication attempts increase.
- A Chosun reporter demonstrated that REA could analyze an open-source document editor in ~7 seconds, with AI coding tools then recreating core editing features and a working interface in under 7 minutes total.
- The NSA's Ghidra, a reverse engineering tool hidden for nearly 20 years and released publicly in 2019, previously made analysis impractical due to missing comments and massive code size; AI has largely overcome this barrier.
- A South Korean developer released "Tangle 2026," which can read and edit HWP/HWPX files used by Hancom's Hangul, representing a domestic attempt at AI-driven software replication.
- Opinions in the industry are split: Dutch developer Pieter Levels argues users will build only the features they need rather than pay subscriptions, while Brazilian developer Fábio Akita counters that AI-generated alternatives contain errors and cannot fully replicate complex commercial products.
- South Korea's Copyright Act permits code analysis for compatibility or operational understanding but prohibits using obtained information beyond those purposes, setting the stage for legal disputes as replication attempts increase.
Qwen publishes official GGUF weights for its 8-billion-parameter vision-language model, Qwen3-VL-8B-Instruct, allowing local inference of image, video, and text workloads through common tools like llama.cpp, Ollama, LM Studio, and vLLM without needing a custom Transformers environment. The model supports a 256K-token context window, 32-language OCR, 2D/3D grounding, GUI agent capabilities, and video timestamp localization, all under an Apache 2.0 license.
- The language model is separated from a multimodal projector (mmproj) file, letting users mix precisions such as a 4-bit language model with an F16 projector for better OCR accuracy
- The Q4_K_M language model plus a quantized projector can run on 16 GB unified-memory systems, though the full 256K context window is impractical on that hardware
- Benchmark scores include DocVQA 96.1, MMMU 69.6, and MMLU-Pro 68.6
- The language model is separated from a multimodal projector (mmproj) file, letting users mix precisions such as a 4-bit language model with an F16 projector for better OCR accuracy
- The Q4_K_M language model plus a quantized projector can run on 16 GB unified-memory systems, though the full 256K context window is impractical on that hardware
- Benchmark scores include DocVQA 96.1, MMMU 69.6, and MMLU-Pro 68.6
Asif Razzaq writes that Google Research has open-sourced Harness², a framework that improves agent performance by recursively refining the surrounding harness—prompts, tools, memory, and orchestration—while keeping model weights frozen. It operates without ground-truth labels, using a Propose, Probe, Compose cycle to test candidate harness edits against observed behavior.
- Weaker executors gained more; GLM-5 beat base Gemini 3.5 Flash on WorkBuddy-Bench after one refinement step
- Parallel recursion at k=5 lifted Gemini under OpenCode to 80.8 on WorkBuddy
- The framework extends the 7-file split from earlier Agentic Harness Engineering work by 1 component
- Requires Linux, Python 3.12+, uv, and Vertex AI for model-backed runs
- Weaker executors gained more; GLM-5 beat base Gemini 3.5 Flash on WorkBuddy-Bench after one refinement step
- Parallel recursion at k=5 lifted Gemini under OpenCode to 80.8 on WorkBuddy
- The framework extends the 7-file split from earlier Agentic Harness Engineering work by 1 component
- Requires Linux, Python 3.12+, uv, and Vertex AI for model-backed runs
